Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks

• URL: http://arxiv.org/abs/2504.21844v1
• Date: Wed, 30 Apr 2025 17:53:08 GMT
• Title: Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
• Authors: William Sutcliffe, Marta Calvi, Simone Capelli, Jonas Eschle, Julián García Pardiñas, Abhijit Mathad, Azusa Uzuki, Nicola Serra,
• Abstract summary: Multiplicities are straining latency and storage requirements at the data acquisition stage.<n>New complications are emerging, including higher background levels and more frequent particle misassociations.<n>We propose a novel Heterogeneous Graph Neural Network architecture featuring unique representations for diverse particle collision relationships.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing luminosity frontier at the Large Hadron Collider is challenging the reconstruction and analysis of particle collision events. Increased particle multiplicities are straining latency and storage requirements at the data acquisition stage, while new complications are emerging, including higher background levels and more frequent particle vertex misassociations. This in turn necessitates the development of more holistic and scalable reconstruction methods that take advantage of recent advances in machine learning. We propose a novel Heterogeneous Graph Neural Network (HGNN) architecture featuring unique representations for diverse particle collision relationships and integrated graph pruning layers for scalability. Trained with a multi-task paradigm in an environment mimicking the LHCb experiment, this HGNN significantly improves beauty hadron reconstruction performance. Notably, it concurrently performs particle vertex association and graph pruning within a single framework. We quantify reconstruction and pruning performance, demonstrate enhanced inference time scaling with event complexity, and mitigate potential performance loss using a weighted message passing scheme.

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